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Scalable Distributed Temporal Reasoning

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Advances in Computer Science and Ubiquitous Computing (CUTE 2017, CSA 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 474))

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Abstract

In this paper, we propose the design and implementation of a large-scale qualitative temporal reasoner, MRQUTER, which can perform reasoning over large Web-scale knowledge bases. This temporal reasoner is built on a Hadoop cluster system using the MapReduce parallel programming framework. It decomposes the entire qualitative temporal reasoning process into several MapReduce jobs and incorporates some optimization techniques into each reasoning job component, implemented using a pair of Map and Reduce functions. Through experiments using large benchmarking temporal knowledge bases, MRQUTER shows high reasoning performance and scalability.

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References

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Acknowledgements

This work was supported by the Technology Innovation Program (No. 10060086, A robot intelligence software framework as an open and self-growing integration foundation of intelligence and knowledge for personal service robots) funded By the Ministry of Trade, industry & Energy (MI, Korea).

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Authors and Affiliations

  1. Kyonggi University, Suwon, Korea

    Jonghoon Kim & Incheol Kim

Authors
  1. Jonghoon Kim
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  2. Incheol Kim
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Corresponding author

Correspondence to Incheol Kim .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science and Technology, Seoul, Korea (Republic of)

    James J. Park

  2. Department of Business Science, University of Salerno, Salerno, Italy

    Vincenzo Loia

  3. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

    Gangman Yi

  4. Department of Multimedia Engineering, Dongguk University, Seoul, Soul-t’ukpyolsi, Korea (Republic of)

    Yunsick Sung

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Kim, J., Kim, I. (2018). Scalable Distributed Temporal Reasoning. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_132

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  • DOI: https://doi.org/10.1007/978-981-10-7605-3_132

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7604-6

  • Online ISBN: 978-981-10-7605-3

  • eBook Packages: EngineeringEngineering (R0)

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